​​ Create own Model and DataSet App - Great for learning (with GUI)

A desktop-based image classification app built using TensorFlow, OpenCV, and Tkinter. This user-friendly GUI tool allows you to create custom datasets, train models, and predict images—all without writing code.

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Features

• Create and manage image classes
• Upload and auto-rename images into categorized folders
• Train a CNN or pretrained model (e.g., MobileNetV2)
• Predict new images with class probabilities
• Correct misclassifications and add feedback to dataset
• Fine-tune the model
• Simple GUI via Tkinter for ease of use

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Installation

1. Clone the Repository and Run Program

git clone https://github.com/jsoncodez/diyai.git
cd diyai


pip install -r requirements.txt


python diyai.py

💡 Usage Directions

<<<<<<< Updated upstream

• Work in Progress...

• Click "Train Model"

• Click "Predict Image" and add image to run prediction.

• Work in Progress...

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💡 Usage Tips

• Make sure each class has at least 10–20 images for decent results.
• Use clear, centered images for better model accuracy.
• You can correct a misclassified image after prediction, and the app will let you save it to the right class folder.
• For better performance, retrain the model occasionally with newly added images.

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